## Fintech Credit Risk Assessment for SMEs: Evidence from China

_IMF Working Papers, September 25, 2020_

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## Bibliographic details
- Authors: Yiping Huang, Longmei Zhang, Zhenhua Li, Han Qiu, Tao Sun, Xue Wang
- Published: September 25, 2020
- Series: IMF Working Papers
- DOI: https://doi.org/10.5089/9781513557618.001

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### Overview
- Promoting credit services to small and medium-size enterprises (SMEs) is a perennial challenge for policy makers globally due to high information costs.
- Recent fintech developments may mitigate this problem by leveraging big data or digital footprints on existing platforms; some BigTech firms have extended short-term loans to millions of small firms.
- This paper analyzes 1.8 million loan transactions of a leading Chinese online bank to compare:
  - the fintech approach: assessing credit risk using big data and machine learning models, and
  - the bank approach: assessing credit risk using traditional financial data and scorecard models.

### Data and methodology
- Sample: 1.8 million loan transactions from a leading Chinese online bank.
- Comparative approaches:
  - Fintech approach: big data and machine learning models.
  - Bank approach: traditional financial data and scorecard models.

### Key findings
- Predictive performance:
  - The fintech approach yields better prediction of loan defaults during normal times and periods of large exogenous shocks, reflecting information and modeling advantages.
- Information advantages:
  - BigTech’s proprietary information can complement or, where necessary, substitute credit history in risk assessment, allowing unbanked firms to borrow.
- Financial inclusion and reach:
  - The fintech approach benefits SMEs that are smaller and in smaller cities, hence complementing the role of banks by reaching underserved customers.
- Broader implication:
  - With more effective and balanced policy support, BigTech lenders could help promote financial inclusion worldwide.

### Policy implications and recommendations
- Recognize that big data and machine learning can improve default prediction relative to traditional scorecard models.
- Consider policy frameworks that enable BigTech lenders to complement banking services while managing risks, to expand access for unbanked and underserved SMEs.
- Design balanced regulation to harness information and modeling advantages of fintech while addressing potential prudential and market-structure concerns.

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_Source: https://www.imf.org/en/publications/wp/issues/2020/09/25/fintech-credit-risk-assessment-for-smes-evidence-from-china-49742_